从非结构化文本数据中提取信息,生成原点-目的地矩阵

Mohamed Mejri, S. Turki, S. Faiz
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引用次数: 0

摘要

在本文中,我们提出了一种通过从网站的非结构化文本数据中提取信息来生成原点和目的地矩阵的方法。我们将这种方法称为“原点-目的地矩阵提取器”,它基于三个主要的信息提取模块:一个事件提取模块,我们试图提取给定文本中包含的任何旅行事件;一个命名实体识别模块,用于恢复和检测与不同目标信息对应的命名实体;最后一个依赖句法分析模块,用于检查提取的实体与检测到的旅行事件之间是否存在相互依赖关系。在一组实际数据上进行的实验表明,该方法具有令人满意的结果,精度达到90%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Production of origin destination matrix by extracting information from unstructured textual data
In this paper, we present an approach for the production of origin destination matrices by extracting information from unstructured textual data of websites. This approach, which we called “Origin Destination Matrix Extractor” is based on three main modules of Information Extraction: an extraction of events module with which we tried to extract any travel events contained in a given text, a named entity recognition module for the recovery and detection of named entities that correspond to the different target information and finally a dependency syntactic analysis module to check the existence of interdependencies between extracted entities and detected travel events. The experiments carried out on a set of real data show that the proposed method gives satisfactory results with a precision of over 90%.
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